Unobtrusive Mattress-Based Identification of Hypertension by Integrating Classification and Association Rule Mining

Abstract

Hypertension is one of the most common cardiovascular diseases, which will cause severe complications if not treated in a timely way. Early and accurate identification of hypertension is essential to prevent the condition from deteriorating further. As a kind of complex physiological state, hypertension is hard to characterize accurately. However, most existing hypertension identification methods usually extract features only from limited aspects such as the time-frequency domain or non-linear domain. It is difficult for them to characterize hypertension patterns comprehensively, which results in limited identification performance. Furthermore, existing methods can only determine whether the subjects suffer from hypertension, but they cannot give additional useful information about the patients’ condition. For example, their classification results cannot explain why the subjects are hypertensive, which is not conducive to further analyzing the patient’s condition. To this end, this paper proposes a novel hypertension identification method by integrating classification and association rule mining. Its core idea is to exploit the association relationship among multi-dimension features to distinguish hypertensive patients from normotensive subjects. In particular, the proposed method can not only identify hypertension accurately, but also generate a set of class association rules (CARs). The CARs are proved to be able to reflect the subject’s physiological status. Experimental results based on a real dataset indicate that the proposed method outperforms two state-of-the-art methods and three common classifiers, and achieves 84.4%, 82.5% and 85.3% in terms of accuracy, precision and recall, respectively.

A novel pulse plethysmograph signal analysis method for identification of myocardial infarction, dilated cardiomyopathy, and hypertension

“…A system to detect Hypertension using six morphological descriptors derived from PPG with 92.31% accuracy is discussed in [31]. Identification of Hypertension patients from ballistocardiograms (BCG) is presented in [32]. The system achieved a mean accuracy of 84.4% using class association rules (CAR) classifier and morphological features.…”

An Integrated MCI Detection Framework Based on Spectral-temporal Analysis

“…Then, each channel in each segment is decomposed using 1-D SWT decomposition into four coefficients corresponding to four frequency bands: f1(0.5-4 Hz), f2(4-8 Hz), f3 (8-16 Hz) and f4 (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32). Those coefficients contain information in both time domain and frequency domain, thus is suitable to obtain spectral-temporal features.…”

“…SVM has been widely used in pattern recognition and regression due to its computational efficiency and good generalization performance [29,30] . The core of the SVM algorithm for binary classification is mapping the input data into a linearly separable space using a kernel function.…”

Escalation of Forecasting Accuracy through Linear Combiners of Predictive Models

“…There have been several applications of FLANN, MLP and higher order neural networks to stock market forecasting [13][14][15][16][17]. Other data mining applications such as classification of domestic violence using deep learning [18], mattress-based identification of hypertension using classification [19] and mining event oriented topics in micro blog stream [20] are found in the literature.…”